About This Project

Methane’s climate impact necessitates scalable biocatalysts for oxidation at low concentrations. sMMO offers a promising solution but is structurally complex, limiting heterologous expression and engineering. We propose to deploy a PLM-assisted directed evolution platform integrating ortholog mining, generative design, in silico screening, and high-throughput fluorescence assays to engineer miniaturized and monomeric sMMO systems for methane capture and conversion.

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Motivating Factor

Methane’s greenhouse impact and rising emissions demand effective mitigation strategies. The direct capture and conversion of methane to methanol is a particularly promising option. Biological methane conversion can capture dilute methane and opens an economically favorable pathway to create a versatile, sustainable feedstock for downstream synthesis of value-added compounds. Soluble MMO (sMMO) is particularly promising due to its potential to oxidize methane at low concentrations (<1000 ppm) and its broad substrate range, which could enable bioprocessing of other substrates. Unlike pMMO, sMMO is soluble and operates independently of copper, simplifying heterologous expression, iterative design, and industrial implementation. Ultimately, protein engineering to enhance sMMO function and diversify its industrial applicability could enable efficient methane capture from landfills, agriculture, or wastewater, and conversion to high‐value compounds [1] [2].

Specific Bottleneck

Engineering sMMO is difficult due to its complexity as a multi‐subunit protein with intricate inter-subunit interactions that make robust heterologous expression challenging. sMMO functionality is well characterized in select systems, but its natural diversity remains poorly explored, leaving us with limited insight into variants that could exhibit high methane affinity, catalytic efficiency, or engineering-friendly architectures. Most engineering efforts have utilized native methanotrophs, with slow growth rates and limited genetic tools, and available variants show insufficient methane affinity. Although heterologous expression has been achieved in E. coli (Bennett et al. 2021), it requires co-expression with chaperones, limiting developability. Powerful new protein engineering tools could address these limitations, but they require hands-on adaptation to the sMMO context. Iterative MMO protein design must also overcome sparse screening data relative to other systems.

Actionable Goals

We believe achieving three key objectives will be sufficient to enable a flexible, robust sMMO system that can be optimized for high methane affinity, catalytic efficiency, and robust expression at scale.

1. Curate a comprehensive library of sMMO and related soluble diiron monooxygenase (SDIMO) variants reflecting natural functional diversity.

2. Develop a “plug-and-play” enzyme design with robust heterologous expression in E. coli that allows screening different combinations of subunit structural variants, catalytic pockets, and active sites. This paves the way to engineer a monomeric, single-plasmid system amenable to directed evolution.

3. Build MMO-specific workflows for a) generative design and in silico screening featuring transfer learning via protein LLMs and other advanced models, 2) active learning-assisted directed evolution modeling tuned to maximize learning from sparse training data.